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Decision Support System (DSS) for Hierarchical Allocation of Resources and Tasks for Disaster Management

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Abstract

After the occurrence of a natural disaster, it is of paramount importance to take efficient measures to reduce the casualties and damage to infrastructure. Resource allocation is a generic problem of assigning available resources to the affected areas to cope with the devastation caused by the disaster. To mitigate the deadly effect of a natural disaster, different resources are essential at the emergency sites. Disaster response activities also need the assignment of various critical tasks to be carried out by different emergency workers at the local level. The individual emergency locations convey their demands for resources and required services to the higher-level authorities. Depending on availability, the higher-level authority allocates resources through successive lower levels to the emergency sites. This paper proposes a model for the hierarchical flow of different resources during disaster management in the Indian context, from the top-level authority to the lower levels. This hierarchical architecture also incorporates the allocation of different essential tasks at the ground level to reduce the effect of a natural disaster locally.

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Data Availability Statement

The authors declare that the data supporting the findings of this study are available within the article.

Abbreviations

\(A_{j}^{T}\) :

The allocation of \(j^{\text {th}}\) resource at TCC level

\(A_{ij}^{M}\) :

The allocation of the \(j^{\text {th}}\) resource to \(i^{\text {th}}\) MCC

\(A_{\hat{i}j}^{M_{i}L}\) :

The allocation of the \(j^{\text {th}}\) resource for \(\hat{i}^{\text {th}}\) LCC unit of \(i^{\text {th}}\) MCC

\(C_{in}^{M}\) :

The relative criticality of the \(n^{\text {th}}\) forecasted parameter of \(i^{\text {th}}\) MCC

\(D_{ij}^{b}\) :

Basic demand of \(j^{\text {th}}\) resource of \(i^{\text {th}}\) MCC considering forecast

\(D_{ij}^{e}\) :

Excess demand of \(j^{\text {th}}\) resource of \(i^{\text {th}}\) MCC considering forecast

\(D_{ij}^{o}\) :

Overall demand of \(j^{\text {th}}\) resource of \(i^{\text {th}}\) MCC considering forecast

\(\eta\) :

Allocation factors such as population density and disaster level at TCC level

\(\hat{\eta }\) :

Allocation factors at TCC level

\(E_{in}^{M}\) :

The value of the \(n^{\text {th}}\) forecasted parameter of \(i^{\text {th}}\) MCC

\(E_{n}^{\text {max}}\) :

The maximum value of the \(n^{\text {th}}\) forecasted parameter

\(f_{ik}\) :

The value of \(k^{\text {th}}\) factor of \(i^{\text {th}}\) MCC

\(f_{i\hat{i}\hat{k}}\) :

The value of \(\hat{k}^{\text {th}}\) factor of \(\hat{i}^{\text {th}}\) LCC of \(i^{\text {th}}\) MCC

\(F_{ij}^{M}\) :

The maximum possible allocation of \(j^{\text {th}}\) resource for the \(i^{\text {th}}\) MCC

i :

MCC index

\(\hat{i}\) :

LCC index

\(\tilde{i}\) :

ES index

j :

Resource item index

k :

Allocation parameter index at TCC level

\(\hat{k}\) :

Allocation parameter index at MCC level

\(l_{i}\) :

Number of LCC unit under \(\hat{i}^{\text {th}}\) MCC

\(L_{\hat{i}}\) :

\(\hat{i}^{\text {th}}\) LCC

m :

Number of MCC units

n :

Forecasted parameter index

\(M_{i}\) :

\(i^{\text {th}}\) MCC

n :

Number of different resources available at TCC level

\(p_{i}^{M}\) :

The allocation weight of \(i^{\text {th}}\) MCC

\(p_{i\hat{i}}^L\) :

The weight of \(\hat{i}^{\text {th}}\) LCC of \(i^{\text {th}}\) MCC

\(P_{T_{x}}\) :

Priority of task \(T_{x}\)

r :

The total number of forecasted parameters

\(R_{j}^T\) :

Amount of \(j^{\text {th}}\) resource available to TCC

\(R_{ij}^{M}\) :

The requirement of \(i^{\text {th}}\) MCC for the \(j^{\text {th}}\) resource

\(R_{\hat{i}j}^{M_{i}L}\) :

The requirement of \(j^{\text {th}}\) resource by \(\hat{i}^{\text {th}}\) LCC of \(i^{\text {th}}\) MCC

\(T_{x}\) :

Task vector consists of requirements of number of GVs, UAVs, and boat

\(w_{k}\) :

The weight of \(k^{\text {th}}\) allocation factor provided at TCC level

\(\hat{w_{k}}\) :

The weight of \(\hat{k}^{\text {th}}\) allocation factor provided at MCC level

\(W_{ij}^{M}\) :

The resource allocation vector of \(j^{\text {th}}\) resource for the \(i^{\text {th}}\) MCC

\(W_{\hat{i}j}^{M_{i}L}\) :

The resource allocation vector of \(j^{\text {th}}\) resources for the \(\hat{i}^{\text {th}}\) LCC of \(i^{\text {th}}\) MCC

\(\zeta _{n}\) :

Relative weight of \(n^{\text {th}}\) forecast parameter

CS:

Critical supply

ES:

Emergency site

GV:

Ground vehicle

LCC:

Low-level control centre

MCC:

Middle-level control centre

NS:

Normal supply

RS:

Relief supply

SL:

Surveillance

ST:

Survivor tracking

TCC:

Top-level control centre

UAV:

Unmanned aerial vehicle

References

  1. GoI-Document (2005) The disaster management act, 2005. https://cdn.s3waas.gov.in/s365658fde58ab3c2b6e5132a39fae7cb9/uploads/2018/04/2018041720.pdf. Date last accessed: 09-04-2022

  2. Dave R (2018) Disaster management in India: Challenges and strategies. Prowess Publishing

  3. Singh O, Kumar M (2017) Flood occurrences, damages, and management challenges in India: a geographical perspective. Arab J Geosci 10(5):1–19

    Article  Google Scholar 

  4. Central-Water-Commission (2019) Water and related statistics. http://www.indiaenvironmentportal.org.in/files/file/water-and-related-statistics-2019.pdf. Date last accessed: 23-03-2022

  5. Gupta S, Javed A, Datt D (2003) Economics of flood protection in India. In: Flood Problem and Management in South Asia, Springer. pp 199–210

  6. Singh O, Kumar M (2013) Flood events, fatalities and damages in India from 1978 to 2006. Nat Hazards 69(3):1815–1834

    Article  Google Scholar 

  7. Central-Water-Commission (2022) Statewise flood damage statistics. http://www.indiaenvironmentportal.org.in/files/file/flood%20damage%20data.pdf. Date last accessed: 23-03-2022

  8. Arora A, Siddiqui MA, Pandey M (2020) Statistical analysis of major flood events during 1980-2015 in middle Ganga plain, Ganga river basin, India. In: Spatial Information Science for Natural Resource Management, IGI Global. pp 225–241

  9. Mahanta R, Das D (2017) Flood induced vulnerability to poverty: Evidence from Brahmaputra valley, Assam, India. International Journal of Disaster Risk reduction 24:451–461

    Article  Google Scholar 

  10. Plate EJ (2002) Flood risk and flood management. J Hydrol 267(1–2):2–11

    Article  Google Scholar 

  11. Hansson K, Danielson M, Ekenberg L (2008) A framework for evaluation of flood management strategies. J Environ Manage 86(3):465–480

    Article  Google Scholar 

  12. Mohapatra P, Singh R (2003) Flood management in India. In: Flood Problem and Management in South Asia. Springer, pp 131–143

  13. Kourgialas NN, Karatzas GP (2011) Flood management and a GIS modelling method to assess flood-hazard areas-a case study. Hydrological Sciences Journal-Journal des Sciences Hydrologiques 56(2):212–225

    Article  Google Scholar 

  14. Rahman M, Di L et al (2017) The state of the art of spaceborne remote sensing in flood management. Nat Hazards 85(2):1223–1248

    Article  Google Scholar 

  15. Liu Z, Huang G (2009) Dual-interval two-stage optimization for flood management and risk analyses. Water Resour Manage 23(11):2141–2162

    Article  Google Scholar 

  16. Fotovatikhah F, Herrera M, Shamshirband S, Chau K-W, Faizollahzadeh Ardabili S, Piran MJ (2018) Survey of computational intelligence as basis to big flood management: Challenges, research directions and future work. Engineering Applications of Computational Fluid Mechanics 12(1):411–437

    Article  Google Scholar 

  17. Iqbal U, Perez P, Li W, Barthelemy J (2021) How computer vision can facilitate flood management: A systematic review. International Journal of Disaster Risk Reduction 53:102030

  18. Billa L, Mansor S, Mahmud AR (2004) Spatial information technology in flood early warning systems: an overview of theory, application and latest developments in Malaysia. Disaster Prevention and Management: An International Journal

  19. Duncan A, Keedwell E, Djordjevic S, Savic D (2013) Machine learning-based early warning system for urban flood management. In: International Conference on Flood Resilience: Experiences in Asia and Europe, University of Exeter

  20. Krzhizhanovskaya VV, Shirshov G, Melnikova NB, Belleman RG, Rusadi F, Broekhuijsen B, Gouldby BP, Lhomme J, Balis B, Bubak M et al (2011) Flood early warning system: design, implementation and computational modules. Procedia Computer Science 4:106–115

    Article  Google Scholar 

  21. Altay N, Green WG III (2006) OR/MS research in disaster operations management. Eur J Oper Res 175(1):475–493

    Article  Google Scholar 

  22. Horita FE, deAlbuquerque JP (2013) An approach to support decision-making in disaster management based on volunteer geographic information (VGI) and spatial decision support systems (SDSS). In: 2013 Tenth ISCRAM, Citeseer

  23. Liashenko O, Kyryichuk D, Krugla N, Lozhkin R (2019) Development of a decision support system for mitigation and elimination the consequences of natural disasters in Ukraine. International Multidisciplinary Scientific GeoConference: SGEM 19(2.1):825–832

  24. Moehrle S, Raskob W (2019) Reusing strategies for decision support in disaster management–a case-based high-level petri net approach. Advances in Artificial Intelligence: Reviews 1

  25. Sati S (2015) Scalable framework for emergency response decision support systems. In: 2015 IEEE International Symposium on Technologies for Homeland Security (HST). IEEE, pp 1–6

  26. Wallace WA, DeBalogh F (1985) Decision support systems for disaster management. Public Administration Review. pp 134–146

  27. Zhou L, Wu X, Xu Z, Fujita H (2018) Emergency decision making for natural disasters: An overview. International Journal of Disaster Risk Reduction 27:567–576

    Article  Google Scholar 

  28. Newman JP, Maier HR, Riddell GA, Zecchin AC, Daniell JE, Schaefer AM, van Delden H, Khazai B, O’Flaherty MJ, Newland CP (2017) Review of literature on decision support systems for natural hazard risk reduction: Current status and future research directions. Environ Model Software 96:378–409

    Article  Google Scholar 

  29. Aifadopoulou G, Chaniotakis E, Stamos I, Mamarikas S, Mitsakis E (2018) An intelligent decision support system for managing natural and man-made disasters. International Journal of Decision Support Systems 3(1–2):91–105

    Article  Google Scholar 

  30. Zamanifar M, Hartmann T (2021) Decision attributes for disaster recovery planning of transportation networks; a case study. Transp Res Part D: Transp Environ 93:102771

  31. Lourenço M, Oliveira LB, Oliveira JP, Mora A, Oliveira H, Santos R (2021) An integrated decision support system for improving wildfire suppression management. ISPRS Int J Geo Inf 10(8):497

    Article  Google Scholar 

  32. Marin G, Modica M, Paleari S, Zoboli R (2021) Assessing disaster risk by integrating natural and socio-economic dimensions: A decision-support tool. Socioecon Plann Sci 77:101032

  33. Yaşa E, TüzünAksu D, Özdamar L (2022) Metaheuristics for the stochastic post-disaster debris clearance problem. IISE Transactions pp 1–22

  34. Vogiatzis C, Pardalos PM (2016) Evacuation modeling and betweenness centrality. In: International Conference on Dynamics of Disasters. Springer, pp 345–359

  35. Korkou T, Souravlias D, Parsopoulos K, Skouri K (2016) Metaheuristic optimization for logistics in natural disasters. In: International Conference on Dynamics of Disasters. Springer, pp 113–134

  36. Matsuno Y, Fukanuma F, Tsuruoka S (2021) Development of flood disaster prevention simulation smartphone application using gamification. In: International Conference on Dynamics of Disasters. Springer, pp 147–159

  37. Kondaveti R, Ganz A (2009) Decision support system for resource allocation in disaster management. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 3425–3428

  38. Park J, Cullen R, Smith-Jackson T (2014) Designing a decision support system for disaster management and recovery. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, SAGE Publications, Los Angeles, CA 58:1993–1997

    Google Scholar 

  39. Hashemipour M, Stuban SM, Dever JR (2017) A community-based disaster coordination framework for effective disaster preparedness and response. Australian Journal of Emergency Management, The 32(2):41–46

    Google Scholar 

  40. Li X, Pu W, Zhao X (2019) Agent action diagram: Toward a model for emergency management system. Simul Model Pract Theory 94:66–99

    Article  Google Scholar 

  41. Wang Z, Zhang J (2019) Agent-based evaluation of humanitarian relief goods supply capability. International Journal of Disaster Risk Reduction 36:101105

  42. Othman SB, Zgaya H, Dotoli M, Hammadi S (2017) An agent-based decision support system for resources’ scheduling in emergency supply chains. Control Eng Pract 59:27–43

    Article  Google Scholar 

  43. Sepúlveda JM, Arriagada IA, Derpich I (2018) A decision support system for distribution of supplies in natural disaster situations. In: 2018 7th International Conference on Computers Communications and Control (ICCCC). IEEE, pp 295–301

  44. Sepúlveda JM, Bull J (2019) A model-driven decision support system for aid in a natural disaster. In: International Conference on Human Systems Engineering and Design: Future Trends and Applications. Springer, pp 523–528

  45. Arora H, Raghu T, Vinze AS (2010) Resource allocation for demand surge mitigation during disaster response. Decision Support System 50:304–315

    Article  Google Scholar 

  46. Fiedrich F, Gehbauer F, Rickers U (2000) Optimized resource allocation for emergency response after earthquake disasters. Saf Sci 35:41–57

    Article  Google Scholar 

  47. Wang F, Pei Z, jun Dong L, Ma J (2020a) Emergency resource allocation for multi-period post-disaster using multi-objective cellular genetic algorithm. IEEE Access 8:82255–82265

  48. Worby CJ, Chang HH (2020) Face mask use in the general population and optimal resource allocation during the Covid-19 pandemic. Nat Commun 11(1):1–9

    Article  Google Scholar 

  49. Rolland E, Patterson RA, Ward K, Dodin B (2010) Decision support for disaster management. Oper Manag Res 3(1–2):68–79

    Article  Google Scholar 

  50. Faiz TI, Vogiatzis C, Noor-E-Alam M (2019) A column generation algorithm for vehicle scheduling and routing problems. Comput Ind Eng 130:222–236

    Article  Google Scholar 

  51. Sun J, Zhang Z (2020) A post-disaster resource allocation framework for improving resilience of interdependent infrastructure networks. Transp Res Part D: Transp Environ 85:102455

  52. Das R, Hanaoka S (2014) An agent-based model for resource allocation during relief distribution. Journal of Humanitarian Logistics and Supply Chain Management 4(2):265–285

    Article  Google Scholar 

  53. Doan XV, Shaw D (2019) Resource allocation when planning for simultaneous disasters. Eur J Oper Res 274(2):687–709

    Article  Google Scholar 

  54. Majumder R, Warier RR, Ghose D (2019) Game theory-based allocation of critical resources during natural disasters. In: 2019 Sixth Indian Control Conference (ICC). IEEE, pp 514–519

  55. Nagurney A, Flores EA, Soylu C (2016) A generalized Nash equilibrium network model for post-disaster humanitarian relief. Transportation Research Part E: Logistics and Transportation Review 95:1–18

    Article  Google Scholar 

  56. Wang Y, Bier VM, Sun B (2019) Measuring and achieving equity in multi-period emergency material allocation. Risk Anal 39(11):2408–2426

    Article  Google Scholar 

  57. Wang J, Shen D, Yu M (2020b) Multiobjective optimization on hierarchical refugee evacuation and resource allocation for disaster management. Mathematical Problems in Engineering 2020

  58. Lechtenberg S, Widera A, Hellingrath B (2017) Research directions on decision support in disaster relief logistics. In: 2017 4th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). IEEE, pp 1–8

  59. Nappi MML, Souza JC (2015) Disaster management: Hierarchical structuring criteria for selection and location of temporary shelters. Nat Hazards 75(3):2421–2436

    Article  Google Scholar 

  60. Ghaffari Z, Nasiri MM, Bozorgi-Amiri A, Rahbari A (2020) Emergency supply chain scheduling problem with multiple resources in disaster relief operations. Transportmetrica A: Transport Science 16(3):930–956

    Article  Google Scholar 

  61. Özdamar L, Demir O (2012) A hierarchical clustering and routing procedure for large scale disaster relief logistics planning. Transportation Research Part E: Logistics and Transportation Review 48(3):591–602

    Article  Google Scholar 

  62. Widener MJ, Horner MW (2011) A hierarchical approach to modeling hurricane disaster relief goods distribution. J Transp Geogr 19(4):821–828

    Article  Google Scholar 

  63. Liberatore F, Ortuño MT, Tirado G, Vitoriano B, Scaparra MP (2014) A hierarchical compromise model for the joint optimization of recovery operations and distribution of emergency goods in humanitarian logistics. Comput Oper Res 42:3–13

    Article  Google Scholar 

  64. Ogra A, Donovan A, Adamson G, Viswanathan K, Budimir M (2021) Exploring the gap between policy and action in disaster risk reduction: a case study from India. International Journal of Disaster Risk Reduction 63:102428

  65. Wescoat JL, Bramhankar R, Murty J, Singh R, Verma P (2021) A macrohistorical geography of rural drinking water institutions in India. Water History 13(2):161–188

    Article  Google Scholar 

  66. Salmoral G, Rivas Casado M, Muthusamy M, Butler D, Menon PP, Leinster P (2020) Guidelines for the use of unmanned aerial systems in flood emergency response. Water 12(2):521

    Article  Google Scholar 

  67. GoI-Document (2018) Institutional arrangements for disaster management (DM). https://cdn.s3waas.gov.in/s38d7d8ee069cb0cbbf816bbb65d56947e/uploads/2018/02/2018022462.pdf. Date last accessed: 21-03-2022

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Acknowledgements

The research work was supported by the EPSRC, EP/P02839X/1 ``Emergency flood planning and management using unmanned aerial systems". The authors would like to thank their colleagues in the EPSRC project partner institutes, University of Exeter, Cranfield University, Indian Institute of Science, Indraprastha Institute of Information Technology Delhi, and the Tata Consultancy Services, for their help and assistance.

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Authors and Affiliations

  1. Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India

    Shuvrangshu Jana, Rudrashis Majumder & Debasish Ghose

  2. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, United Kingdom

    Prathyush P. Menon

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  1. Shuvrangshu Jana
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Jana, S., Majumder, R., Menon, P.P. et al. Decision Support System (DSS) for Hierarchical Allocation of Resources and Tasks for Disaster Management. Oper. Res. Forum 3, 37 (2022). https://doi.org/10.1007/s43069-022-00148-6

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